Matching pursuit decompositions have been employed for signal coding. For this purpose, matching pursuit coefficients need to be quantized. However, their behavior has been shown to be chaotic in some cases; posing difficulties to their modeling and quantizer design. In this work, a different approach is presented. Instead of trying to model the statistics of matching pursuit coefficients, the statistics of the angle between the residue signal and the element selected in each iteration of the matching pursuit are studied, what allows to model matching pursuits coefficients indirectly. This approach results in a simple statistical model. This is so because one observes that the statistics of such angles do not vary substantially after the first matching pursuit iteration, and can be approximately modeled as independent and identically distributed. Moreover, it is also observed that the probability density functions of matching pursuit angles are reasonably modeled by a single probability density function. This function depends only on the dictionary employed and not on the signal source. The derived statistical model is validated by employing it to design Lloyd-Max quantizers for matching pursuit coefficients. The Lloyd-Max quantizers obtained show good ratexdistortion performance when compared to the state-of-the-art methods.